
The MEC server at the edge cloud first selects a computing task, e.g., vehicular traffic analytics, along with task requirements, e.g., task classification or task prediction, and learning parameters, e.g., learning rate. Then, the MEC server chooses a subset of devices as learning clients for collaborative training. In practice, the MEC server can select different subsets of devices over different update rounds by using a suitable client scheduling mechanism to improve its training quality [1]}.

The server initializes a training model and sends it to all clients to create a new round of training. Here, each client trains the model using its own dataset and calculates a new update.

Each client uploads its computed update to the MEC server for global computation. By aggregating all updates from clients, the server constructs a new global model in a fashion that minimizes a loss function [2]}.

The server then broadcasts the computed global model to all clients for the next round of training. The FL process is iterated until the global loss function converges or the desired accuracy is achieved.

